In this project, we aim to predict house prices for 200 apartments in Pune city. We will use various regression models, such as Linear Regression, Random Forest, XGBoost, and multi-layer perceptron (MLP) models using scikit-learn and TensorFlow. The goal is to help predict house prices based on different property features.

We have a dataset with around 200 rows and 17 variables that influence the target variable, which is the house price.

• Language: Python
• Libraries: scikit-learn, pandas, NumPy, matplotlib, seaborn, xgboost

• Import required libraries and load the dataset.
• Perform preliminary data exploration.
• Identify and remove outliers.
• Remove redundant feature columns.
• Handle missing values.
• Regularize categorical columns.
• Save the cleaned data.

• Import the cleaned dataset.
• Convert binary columns to dummy variables.
• Perform feature engineering.
• Conduct univariate and bivariate analysis.
• Check for correlations.
• Select relevant features.
• Scale the data.
• Save the final updated dataset.

• Prepare the data.
• Split the dataset into training and testing sets.
• Build various regression models, including Linear Regression, Ridge Regression, Lasso Regressor, Elastic Net, Random Forest Regressor, XGBoost Regressor, K-Nearest Neighbours Regressor, and Support Vector Regressor.

• Assess model performance using metrics like Mean Squared Error (MSE) and R2 score.
• Create residual plots for both training and testing data.

• Perform grid search and cross-validation for the chosen regressor.

• Fit the model and make predictions on the test data.

• Check for feature importance to identify the most influential factors in predicting house prices.

• Compare the performance of different models to choose the best one.

• Build MLP Regression models using both scikit-learn and TensorFlow.

To run this project, follow these steps:

1. Install the required libraries listed in requirements.txt.
2. Execute the code in the src folder.